64 citations · 130 across the 10 of their papers we have counts for
9 papers · 1 filter
Weakly Supervised Test-Time Domain Adaptation for Object Detection
Anh-Dzung Doan, Bach Long Nguyen, Terry Lim +6
Prior to deployment, an object detector is trained on a dataset compiled from a previous data collection campaign. However, the environment in which the object detector is deployed…
Social-MAE: Social Masked Autoencoder for Multi-person Motion Representation Learning
Mahsa Ehsanpour, Ian Reid, Hamid Rezatofighi
For a complete comprehension of multi-person scenes, it is essential to go beyond basic tasks like detection and tracking. Higher-level tasks, such as understanding the interaction…
JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environments
Duy-Tho Le, Chenhui Gou, Stavya Datta +4
Autonomous robot systems have attracted increasing research attention in recent years, where environment understanding is a crucial step for robot navigation, human-robot interacti…
Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes
Raphael Lafargue, Yassir Bendou, Bastien Pasdeloup +4
When training data is scarce, it is common to make use of a feature extractor that has been pre-trained on a large base dataset, either by fine-tuning its parameters on the ``targe…
Deep Learning Features at Scale for Visual Place Recognition
Zetao Chen, Adam Jacobson, Niko Sunderhauf +5
The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using gen…
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
Dong Gong, Jie Yang, Lingqiao Liu +5
Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior,…